摘要
针对土壤含水量预测模型存在难以解决非线性复杂特征、易陷入局部极小值等问题,构建融合Transformer和LSTM的土壤含水量深度学习预测模型(Transformer-LSTM).采集山东省青岛市黄岛区丁家寨村蓝莓(Vaccinium spp.)生产区冷棚与露天2个站点的蓝莓根区土壤和气象数据作为建模数据,根据皮尔逊相关性和偏自相关性分析选择模型的数据输入特征与输入长度,与单一的Transformer模型和LSTM模型进行对比分析,评估模型对土壤含水量的预测性能.结果表明,Transformer-LSTM模型在预测精度上均优于单一的Transformer模型和LSTM模型,Transformer-LSTM模型的平均绝对误差(MAE)、均方根误差(RMSE)、平均绝对百分比误差(MAPE)、决定系数(R2)分别为 0.245 9、0.572 0、0.012 1、0.960 6.Transformer-LSTM模型可以更全面地提取蓝莓种植环境因子输入序列中的特征信息,有效提升土壤含水量因子预测精度和水平.
Abstract
A deep learning prediction model for soil moisture content(transformer LSTM)was constructed,which integrated transform-er and LSTM,to address the difficulties in solving nonlinear and complex features,as well as the tendency to fall into local minima in the soil moisture prediction model.Soil and meteorological data from the blueberry(Vaccinium spp.)root zone of two stations,cold shed and outdoor,in the blueberry production area of Dingjiazhai Village,Huangdao District,Qingdao City,Shandong Province,were col-lected as modeling data,based on Pearson correlation and partial autocorrelation analysis,the data input characteristics and input length of the selected model were compared and analyzed with a single transformer model and LSTM model to evaluate the predictive performance of the model on soil moisture content.The results showed that the transformer LSTM model outperformed both the single transformer model and the LSTM model in prediction accuracy.The mean absolute error(MAE),root mean square error(RMSE),mean absolute percentage error(MAPE),and coefficient of determination(R2)of the transformer LSTM model were 0.245 9,0.572 0,0.012 1,and 0.960 6,respectively.The transformer LSTM model could more comprehensively extract feature information from the in-put sequence of blueberry planting environmental factors,effectively improving the accuracy and level of soil moisture factor prediction.
基金项目
新疆维吾尔自治区重点研发任务专项(2022B02049-1-3)
中国农业科学院创新工程任务项目(HT20220570)
NETL
NSTL
万方数据